Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors

Fields, Brandon; Demirjian, Natalie L.; Hwang, Darryl; Varghese, Bino; Cen, Steven; Lei, Xiaomeng; Desai, Bhushan; Duddalwar, Vinay; Matcuk, George R.

doi:10.1007/s00330-021-07914-w

Cited by 25 publications

(35 citation statements)

References 52 publications

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“…Improvement in differential diagnosis of malignancy have been reported by several authors [ 97 , 98 ]. In particular radiomics-based differentiation between soft-tissue lipoma and well-differentiated liposarcoma [ 99 , 100 ] was demonstrated despite similar radiologic and pathologic presentation, often requiring molecular analysis of MDM2 amplification status; interestingly, superior performance of a machine-learning classifier as compared to trained radiologists has been shown [ 101 ].…”

Section: Resultsmentioning

confidence: 99%

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

Cancers

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Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

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Section: Resultsmentioning

confidence: 99%

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

Cancers

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“…In order to avoid over-optimized estimation, 10-fold cross validation was applied to assess both the Rad-score and the radiological model. The 10-fold cross validation has been a commonly used method in previously reported studies to avoid confounders arisen from single data assignment (23)(24)(25). In the 10-fold cross validation, the patients were randomly allocated to training and validation sets in a 9:1 ratio for 10 times.…”

Section: Model Validation and Assessmentmentioning

confidence: 99%

CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

et al. 2021

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ObjectivesTo investigate the value of CT-based radiomics analysis in preoperatively discriminating pancreatic mucinous cystic neoplasms (MCN) and atypical serous cystadenomas (ASCN).MethodsA total of 103 MCN and 113 ASCN patients who underwent surgery were retrospectively enrolled. A total of 764 radiomics features were extracted from preoperative CT images. The optimal features were selected by Mann-Whitney U test and minimum redundancy and maximum relevance method. The radiomics score (Rad-score) was then built using random forest algorithm. Radiological/clinical features were also assessed for each patient. Multivariable logistic regression was used to construct a radiological model. The performance of the Rad-score and the radiological model was evaluated using 10-fold cross-validation for area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.ResultsTen screened optimal features were identified and the Rad-score was then built based on them. The radiological model was built based on four radiological/clinical factors. In the 10-fold cross-validation, the Rad-score was proved to be robust and reliable (average AUC: 0.784, sensitivity: 0.847, specificity: 0.745, PPV: 0.767, NPV: 0.849, accuracy: 0.793). The radiological model performed slightly less well in classification (average AUC: average AUC: 0.734 sensitivity: 0.748, specificity: 0.705, PPV: 0.732, NPV: 0.798, accuracy: 0.728.ConclusionsThe CT-based radiomics analysis provided promising performance for preoperatively discriminating MCN from ASCN and showed good potential in improving diagnostic power, which may serve as a novel tool for guiding clinical decision-making for these patients.

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“…Yet another indication is that problem statements of most studies do not reflect real clinical scenarios. Most studies aim at distinguishing two to three specific tumour entities [10,16,34,43,[46][47][48] or assessing tumour malignancy [15,18,19,22,26,28,32,33,35,36,39,40,42,44,45]. If one fed a third entity to a two-entity classifier, the model would try to fit the third entity into one of the first two entity classes.…”

Section: Discussionmentioning

confidence: 99%

“…Likewise, radiomics appears to be on demand. 42.1% of articles (16) utilised radiomic data [15,17,19,21,23,27,28,33,34,37,43,[45][46][47][48]51], while only 17.5% (7) integrated CT, 25.0% (10) X-ray and 2.5% (1) US. With radiomics, a large number of quantitative features can be extracted from imaging data.…”

Section: Mri and Radiomicsmentioning

confidence: 99%

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

et al. 2022

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Musculoskeletal malignancies are a rare type of cancer. Consequently, sufficient imaging data for machine learning (ML) applications is difficult to obtain. The main purpose of this review was to investigate whether ML is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies and what the respective reasons for this might be. A scoping review was conducted by a radiologist, an orthopaedic surgeon and a data scientist to identify suitable articles based on the PRISMA statement. Studies meeting the following criteria were included: primary malignant musculoskeletal tumours, machine/deep learning application, imaging data or data retrieved from images, human/preclinical, English language and original research. Initially, 480 articles were found and 38 met the eligibility criteria. Several continuous and discrete parameters related to publication, patient distribution, tumour specificities, ML methods, data and metrics were extracted from the final articles. For the synthesis, diagnosis-oriented studies were further examined by retrieving the number of patients and labels and metric scores. No significant correlations between metrics and mean number of samples were found. Several studies presented that ML could support imaging-driven diagnosis of musculoskeletal malignancies in distinct cases. However, data quality and quantity must be increased to achieve clinically relevant results. Compared to the experience of an expert radiologist, the studies used small datasets and mostly included only one type of data. Key to critical advancement of ML models for rare diseases such as musculoskeletal malignancies is a systematic, structured data collection and the establishment of (inter)national networks to obtain substantial datasets in the future. Key Points • Machine learning does not yet significantly impact imaging-driven diagnosis for musculoskeletal malignancies compared to other disciplines such as lung, breast or CNS cancer. • Research in the area of musculoskeletal tumour imaging and machine learning is still very limited. • Machine learning in musculoskeletal tumour imaging is impeded by insufficient availability of data and rarity of the disease.

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Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors

Cited by 25 publications

References 52 publications

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

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